PaddlePaddle / PaddleSpeech
- суббота, 25 декабря 2021 г. в 00:33:54
An Easy-to-use Speech Toolkit including SOTA ASR pipeline, influential TTS with text frontend and End-to-End Speech Simultaneous Translation.
(简体中文|English)
PaddleSpeech is an open-source toolkit on PaddlePaddle platform for a variety of critical tasks in speech and audio, with the state-of-art and influential models.
Input Audio | Recognition Result |
---|---|
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I knocked at the door on the ancient side of the building. |
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我认为跑步最重要的就是给我带来了身体健康。 |
For more synthesized audios, please refer to PaddleSpeech Text-to-Speech samples.
Input Text | Output Text |
---|---|
今天的天气真不错啊你下午有空吗我想约你一起去吃饭 | 今天的天气真不错啊!你下午有空吗?我想约你一起去吃饭。 |
Via the easy-to-use, efficient, flexible and scalable implementation, our vision is to empower both industrial application and academic research, including training, inference & testing modules, and deployment process. To be more specific, this toolkit features at:
We strongly recommend our users to install PaddleSpeech in Linux with python>=3.7.
Up to now, Linux supports CLI for the all our tasks, Mac OSX and Windows only supports PaddleSpeech CLI for Audio Classification, Speech-to-Text and Text-to-Speech. To install PaddleSpeech
, please see installation.
Developers can have a try of our models with PaddleSpeech Command Line. Change --input
to test your own audio/text.
Audio Classification
paddlespeech cls --input input.wav
Automatic Speech Recognition
paddlespeech asr --lang zh --input input_16k.wav
Speech Translation (English to Chinese)
(not support for Mac and Windows now)
paddlespeech st --input input_16k.wav
Text-to-Speech
paddlespeech tts --input "你好,欢迎使用飞桨深度学习框架!" --output output.wav
Text Postprocessing
paddlespeech text --task punc --input 今天的天气真不错啊你下午有空吗我想约你一起去吃饭
For more command lines, please see: demos
If you want to try more functions like training and tuning, please have a look at Speech-to-Text Quick Start and Text-to-Speech Quick Start.
PaddleSpeech supports a series of most popular models. They are summarized in released models and attached with available pretrained models.
Speech-to-Text contains Acoustic Model, Language Model, and Speech Translation, with the following details:
Speech-to-Text Module Type | Dataset | Model Type | Link |
---|---|---|---|
Speech Recogination | Aishell | DeepSpeech2 RNN + Conv based Models | deepspeech2-aishell |
Transformer based Attention Models | u2.transformer.conformer-aishell | ||
Librispeech | Transformer based Attention Models | deepspeech2-librispeech / transformer.conformer.u2-librispeech / transformer.conformer.u2-kaldi-librispeech | |
Alignment | THCHS30 | MFA | mfa-thchs30 |
Language Model | Ngram Language Model | kenlm | |
TIMIT | Unified Streaming & Non-streaming Two-pass | u2-timit | |
Speech Translation (English to Chinese) | TED En-Zh | Transformer + ASR MTL | transformer-ted |
FAT + Transformer + ASR MTL | fat-st-ted |
Text-to-Speech in PaddleSpeech mainly contains three modules: Text Frontend, Acoustic Model and Vocoder. Acoustic Model and Vocoder models are listed as follow:
Text-to-Speech Module Type | Model Type | Dataset | Link |
---|---|---|---|
Text Frontend | tn / g2p | ||
Acoustic Model | Tacotron2 | LJSpeech | tacotron2-ljspeech |
Transformer TTS | transformer-ljspeech | ||
SpeedySpeech | CSMSC | speedyspeech-csmsc | |
FastSpeech2 | AISHELL-3 / VCTK / LJSpeech / CSMSC | fastspeech2-aishell3 / fastspeech2-vctk / fastspeech2-ljspeech / fastspeech2-csmsc | |
Vocoder | WaveFlow | LJSpeech | waveflow-ljspeech |
Parallel WaveGAN | LJSpeech / VCTK / CSMSC | PWGAN-ljspeech / PWGAN-vctk / PWGAN-csmsc | |
Multi Band MelGAN | CSMSC | Multi Band MelGAN-csmsc | |
Style MelGAN | CSMSC | Style MelGAN-csmsc | |
HiFiGAN | CSMSC | HiFiGAN-csmsc | |
Voice Cloning | GE2E | Librispeech, etc. | ge2e |
GE2E + Tactron2 | AISHELL-3 | ge2e-tactron2-aishell3 | |
GE2E + FastSpeech2 | AISHELL-3 | ge2e-fastspeech2-aishell3 |
Audio Classification
Task | Dataset | Model Type | Link |
---|---|---|---|
Audio Classification | ESC-50 | PANN | pann-esc50 |
Punctuation Restoration
Task | Dataset | Model Type | Link |
---|---|---|---|
Punctuation Restoration | IWLST2012_zh | Ernie Linear | iwslt2012-punc0 |
Normally, Speech SoTA, Audio SoTA and Music SoTA give you an overview of the hot academic topics in the related area. To focus on the tasks in PaddleSpeech, you will find the following guidelines are helpful to grasp the core ideas.
The Text-to-Speech module is originally called Parakeet, and now merged with this repository. If you are interested in academic research about this task, please see TTS research overview. Also, this document is a good guideline for the pipeline components.
To cite PaddleSpeech for research, please use the following format.
@misc{ppspeech2021,
title={PaddleSpeech, a toolkit for audio processing based on PaddlePaddle.},
author={PaddlePaddle Authors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleSpeech}},
year={2021}
}
You are warmly welcome to submit questions in discussions and bug reports in issues! Also, we highly appreciate if you are willing to contribute to this project!
Besides, PaddleSpeech depends on a lot of open source repositories. See references for more information.
PaddleSpeech is provided under the Apache-2.0 License.